Demonstration notebooks for Machine Learning
scGEAToolbox: Matlab toolbox for single-cell gene expression analyses
The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.
DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
Comparison of various Dimensionality Reduction techiniques and Visualization of the same.
A fast units and dimensions library with support for static dimensionality checking and protobuffer serialization.
Code for the paper, "The Curse of Dimensionality: Inside Out", DOI = 10.13140/RG.2.2.29631.36006.
Just a bunch of tools made in TypeScript.
Visualization and embedding of large datasets using various Dimensionality Reduction (DR) techniques such as t-SNE, UMAP, PaCMAP & IVHD. Implementation of custom metrics to assess DR quality with complete explaination and workflow.
Return the shape of a provided ndarray.
montecarlo methods
Minimal PCA library based on numpy and practical examples of dimensionality reduction use of the principal components in ETF market analysis.
Return the shape of a provided ndarray.
A Python project for generating and testing bipolar, multi-dimensional number sequences, representing scope and essence through layers of dimensions.
My talk to UFRJ Ecology Graduate Program
Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with.
Given a list of numbers in a file, estimates the dimensional expansivity of that dataset in a binary hamming space
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